Studies have shown that microRNAs (miRNAs) play a vital role in tumor progression and patients’ prognosis. Therefore, we aimed to construct a miRNA model for forecasting the survival of hepatocellular carcinoma (HCC) patients. The gene expression data of 433 patients with HCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus public databases were remined by survival analysis and receptor manipulation characteristic curve (ROC). A prognostic model including six miRNAs (hsa‐mir‐26a‐1‐3p, hsa‐mir‐188‐5p, hsa‐mir‐212‐5p, hsa‐mir‐149‐5p, hsa‐mir‐105‐5p, and hsa‐mir‐132‐5p) were constructed in the training dataset (TCGA, n = 333). HCC patients were stratified into a high‐risk group and a low‐risk group with significantly different survival (median: 2.75 vs. 8.93 years, log‐rank test p < .001). Then we proved its performance of stratification in another independent dataset (GSE116182, median: 2.55 vs 6.96 years, log‐rank test p = .008). Cox regression analysis showed that the prognostic model was an independent prognostic indicator for HCC patients. Then time‐dependent ROC analyses were performed to test the prognostic ability of the model with that of TNM staging, we found the model had a better performance, especially at 5 years (AUC = 0.76). Functional prediction showed that the genes targeted by the six prognostic miRNAs in the prognostic model were highly expressed in the P53‐related pathway. In conclusion, we constructed a prognostic miRNA model that could indicate the survival of HCC patients.
Glioblastoma (GBM) has become the most aggressive primary brain tumor in the world. Patients with GBM usually have a poor prognosis. The median survival times of GBM patients retain less than 2 years. Thus, it is urgent to investigate the molecular mechanism of GBM. Recently, studies have demonstrated that transcription factors (TFs) participate in cancer pathology by regulating long noncoding RNAs (lncRNAs). However, the functional and regulatory roles of TF‐lncRNA crosstalks are still unclear. In this study, we constructed a global lncRNA‐TF network (GLTN) based on competing endogenous RNA. As a result, some topological features of GLTN were identified. A known GBM lncRNA MCM3AP‐AS1 showed multiple central topological features in GLTN. Furthermore, we identified hub genes and extracted the hub‐hub pairs from GLTN to form a hub associated lncRNA‐TF network (HALTN). Results showed that a risk model combined with multiple hubs had a significant effect on prognosis. Additionally, we performed module searching and two functional modules from HALTN were identified, which were confirmed as risk factors of GBM. More importantly, we also identified some core lncRNA‐TF crosstalks that might form feedback loops to control the biological processes in GBM. Our results demonstrated that the synergistic, competitive lncRNA‐TF crosstalks played an important role in pathological processes of GBM, and had strong effect on prognosis. All these results can help us to uncover the molecular mechanism and provide a new therapeutic target for GBM.
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